Our goal : Investigate how/if selected global socio-economic factors are correlated with the spread of Covid-19

photo from::https://sdbi.in/artificial-intelligence-and-big-data-to-control-covid-19"

• Covid-19 data from the github repository of John Hopkins University
• Demographic and social factors on a country basis from gapminder and worldbank
• Latitude and longitude data for countries is from the “maps” package dataset

Cleaning: Issues with dataset:

• Timeseries data in very wide format
• Country names were not consistent over data sources
• Multiple files had to be combined
• Recovered are reported inconsistently

Augmentation:

• Calculate cases, deaths, and recoveries per 100K citizens
• Additional augmentations with rolling means and new cases per day
• For shiny app: Join latitude and longitude data to the country level Covid data.

• Initial exploratory data analysis (eda)
• Modelling:
  • PCA analysis: any trends, clusters, outliers in cases via countries and dependent variables?
  • 2 linear regression models (\(y=\beta_0 + \beta_1 x_1+ \beta_2 x_2 + \epsilon\)):
    • \(x_1\) = Pop % above 65, \(x_2\) = Urban pop %. Grouped by income level
    • \(x_1\) = GDP, \(x_2\) = Pop density. Grouped by region
• Exploring Covid “waves” and case fatality

PCA done on continous socio-economic features

• PC 1 explains differences in cases fairly well
• Some countries are outliers in this projection

Deaths as Function of Population % above 65 years and Population % living in urban areas.

Significant slopes

• population % above 65 years with high, upper middle and lower middle income
• population % in urban areas with upper middle income.

What is a good criteria for a wave?

• We found that a weekly increase 10% in deaths is a good identifier

Do Covid-19 waves appear to be synchronized in different countries?

https://christianjohansen.shinyapps.io/covid-19_app/

In general from eda and linear regression, data suggests that more developed countries are hit “harder” by Covid-19.

• This could be due to less developed/lower income countries having a younger avg. age (higher general mortality rates), thus younger populations are less severely influenced by infection.
• However, there may be data quality issues and under reporting between countries.

Things to note

• Correlations do not infer causation.
• Linear model is not dealing with collinearity of features.

We see that Covid “waves” are not synchronous between global regions.

• Some regions had very visually distinct peaks of waves over time (Europe & Central Asia), while other were less distinct (Asia & Pacific)
• The region Europe & Central Asia had the highest percentage of countries in waves at a certain time. At the highest peak almost 70% of the countries in the region were in a wave.